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One of our team members graduated last December. AINNA is his first employer, and at the time of writing he had been with us for roughly 3 months.

With AINNA智能体 AI, he built a complete financial 系统 — one that was later assessed at TRL9 through MOSTI, the highest level in the 科技 Readiness Level framework.

What holds my attention is not the 系统 itself, and not its price tag.

It is who was able to build it, and how quickly that capability can be acquired once people work alongside AI 智能体.

At an event hosted by the Saudi Embassy recently, I put this to an acquaintance I had just met:

Going forward, for certain 系统 in the manufacturing industry, a Chemical 工程 graduate may find it easier to build an IT 系统 for a plant than an IT graduate who does not understand the manufacturing process.

Not because the Chemical Engineer codes better.

But because coding is getting cheaper, and AI keeps making it easier to do.

A Chemical Engineer already understands process flow, pressure, temperature, chemical reactions, pumps, valves, safety, quality, and what actually happens on the production floor.
AI 智能体, in turn, can help them build databases, dashboards, APIs, automation, monitoring 系统, and applications.

So the question for the future is no longer:
“Who codes best?”

But rather:
“Who best understands the real problem that needs to be solved?”

AI does not remove the importance of IT.

But it may move IT from the party that builds everything to the enabler that lets domain experts build things far faster.

And that may be the real challenge for the next generation:
时间 the technical barrier falls, domain knowledge, problem solving, and the ability to understand the 真实世界 become the expensive line items.